Image processing refers to any and all activities that acquire, manipulate, store and display images. Many specific useful applications arise in, for example, photography, medical imaging and satellite imaging.
Images are typically acquired with a camera or scanner that exposes sensors or film that register a single spectral band or multiple spectral bands of incident radiation from a scene to be imaged. Monochrome images are examples of images acquired using a single spectral band. Color images are examples of images acquired using several spectral bands that represent blue, green, and red signals. In general, multi-spectral images contain more useful information than single spectral band images. However, sensors or film used in cameras or scanners that acquire multi-spectral images are more complex and expensive than sensors or film used to acquire single spectral band images. The cost of cameras and sensors to acquire high quality multi-spectral images at high resolutions may be extremely expensive.
The performance of an image processing system can be measured in many ways. One measure is “resolution,” or the ability to distinguish different scene objects or materials in the processed image. “High resolution” means the processed image shows different objects that are closely spaced in the scene. Such “high resolution” is more properly termed “high spatial resolution.” The term “high spectral resolution” refers to the system's ability to distinguish some elements that are closely related in electromagnetic wavelength.
Because of the high cost of acquiring high spatial resolution, multi-spectral images directly, it would be desirable to acquire multi-spectral images using lower resolution cameras and to increase the resolution of the acquired images in a less expensive way.
One conventional technique for increasing the spatial resolution of a low resolution image is described in a paper entitled “Adaptive Image Sharpening using Multiresolution Representations” by A. Evan Iverson and James R. Lersch, SPIE Volume 2231, 5 Apr. 1994. This technique uses a high-resolution image acquired from a single band sensor to enhance the resolution of a co-registered, low resolution, multi-spectral band image. Specifically, the technique disclosed therein relies upon computationally demanding and complex steps of creating Gaussian and Laplacian pyramids to find relationships between the high-resolution and low resolution images that are then used to map pixels from the high resolution image to the low resolution image, creating a synthetic image with improved spatial resolution.
It is desirable to provide a method of increasing the spatial resolution of a low-resolution, multi-spectral image that is fast and less computationally demanding than the Iverson and Lersch technique. It is further desirable to provide a method of increasing the spatial resolution of a low-resolution, multi-spectral image using at least one high resolution image in a way that closely approximates the spectral content of interest from the low resolution image.
Another prior art technique is the Projective Pan Sharpening Algorithm (published by John Lindgren and Steven Kilston as paper #2818-26, SPIE '96, Denver, Colo.). This technique assumes nearly complete spectral overlap between a panchromatic band and the multi-spectral bands it is used to sharpen. It also assumes a linear relationship between intensities in the pan and multi-spectral bands. The present technique requires neither of these assumptions.
The Pradines algorithm and the Price algorithm, referenced in the Lindgren and Kilston paper, each also are based on the linearity assumption, whereas the present technique can have different relationships for different spectral signatures.